Using a Hierarchical Classification Model to Predict Protein Tertiary Structure

To predict protein tertiary structure accurately is helpful for understanding the functions of proteins. In this study, a hierarchical classification method based on flexible neural tree was proposed to predict the structures, in which the tier classifiers were flexible neural trees due to their excellent performances. In order to classify the structures, three types of feature are used, i.e. the tripeptide composed of dimension reduction, the pseudo amino acid composition and the position information of amino acid residues. To evaluate our method, the 640 data set was used in this investigation. The experimental results suggest that our method overwhelms several representative approaches to predicting protein tertiary structure.